Design for Additive Manufacturing (DfAM)
Description
Advanced design principles and methodologies specifically tailored for additive manufacturing technologies to optimize part performance, manufacturability, and cost-effectiveness.
Learning Objectives
Students will understand design freedom and constraints unique to additive manufacturing, apply topology optimization and generative design techniques, implement design rules for different AM technologies, optimize part orientation and support requirements, integrate multiple functions into single parts, design lattice structures and cellular materials, and develop cost-effective designs that leverage AM advantages while avoiding common pitfalls.
Topics (12)
FDM design rules and limitations, SLA precision and feature resolution, SLS self-supporting capabilities, metal AM design considerations, ceramic AM constraints, and material-specific design adaptations.
Generative design principles, AI and machine learning in design, constraint definition, design space exploration, Autodesk Generative Design, nTopology, and multi-objective optimization approaches.
Lattice structure fundamentals, unit cell design, TPMS structures, mechanical properties of lattices, scaling laws, design software tools, manufacturing considerations, and application examples in aerospace and automotive.
Assembly reduction strategies, functional integration techniques, embedded features design, moving parts in single print, snap-fits and living hinges, integrated cooling channels, and design for serviceability.
Self-supporting design principles, overhang angle optimization, bridge design techniques, support-free orientation strategies, geometry modification for support reduction, and technology-specific support elimination methods.
Surface roughness prediction, build orientation effects on surface quality, as-built vs machined surfaces, design for post-processing, accessibility for finishing operations, and hybrid manufacturing approaches.
Multi-scale design concepts, macro-meso-micro scale interactions, material microstructure design, functionally graded materials, scale-dependent properties, and hierarchical structures.
Design validation workflows, simulation-driven design, physical testing protocols, design iteration cycles, failure analysis, design margin considerations, and certification pathways for critical applications.
Cost modeling for AM, material usage optimization, build time minimization, post-processing cost considerations, batch optimization, design-to-cost methodologies, and economic trade-off analysis.
AM design freedom vs traditional constraints, complexity for free concept, geometric possibilities in AM, design rule comparison across technologies, and mindset transformation from subtractive to additive design thinking.
Topology optimization fundamentals, load case definition, boundary conditions, optimization objectives, software tools (Altair OptiStruct, nTopology), bionic structures, and AM-specific optimization constraints.
Biomimicry fundamentals, natural structure analysis, honeycomb and bone-like structures, branching patterns, surface textures from nature, functional mimicry, and bio-inspired optimization algorithms.